
# Imports
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as pyo
import seaborn as sns
import matplotlib.pyplot as plt

import folium
from folium import Circle
import geopy
import watermark
%reload_ext watermark 
%watermark --iversions
# pode haver outras importações do decorrer do notebook

geopy     : 2.2.0
numpy     : 1.21.5
seaborn   : 0.11.2
matplotlib: 3.5.1
watermark : 2.3.1
folium    : 0.12.1.post1
plotly    : 5.6.0
pandas    : 1.4.2


# Configurações de exibição do pandas
pd.options.display.max_columns = 180
pd.options.display.max_rows = 180


# Carregando os dados
df = pd.read_csv('data/cost-of-living.csv')
df.shape

(55, 161)


df.head(3)


df = df.T
df.rename(columns=df.iloc[0], inplace=True)
df.drop(df.index[0], inplace=True)
df = df.reset_index()
df.rename(columns={'index':'Location'}, inplace=True)
df.head()


# As colunas foram transformadas para objeto. Transformando novamente como numéricas:
df.iloc[:,1:] = df.iloc[:,1:].astype(float)


# Extraindo latitude e longitude
# Criando localizador
locator = geopy.Nominatim(user_agent='myGeocoder')
location = locator.geocode('Saint Petersburg, Russia')
print(f'Latitude:{location.latitude}, Logitude:{location.longitude}')

Latitude:59.917857350000006, Logitude:30.380619357025516


# limitador de tempo para requisição API
from geopy.extra.rate_limiter import RateLimiter
geocode = RateLimiter(locator.geocode, min_delay_seconds=1)


df['location'] = df['Location'].apply(geocode)
df['point'] = df['location'].apply(lambda loc: tuple(loc.point) if loc else None)
df[['latitude','longitude','altitude']] = pd.DataFrame(df['point'].tolist(), index=df.index)
df.drop(columns=['point','location','altitude'], inplace=True)
# Visualizando transformação:
df.iloc[:5,-3:]


# Selecionando colunas para criação de atributos com as médias
def food(df):
    return int(round((df[['Meal, Inexpensive Restaurant',
       'Domestic Beer (0.5 liter draught)',
       'Imported Beer (0.33 liter bottle)', 'Coke/Pepsi (0.33 liter bottle)',
       'Water (0.33 liter bottle) ', 'Milk (regular), (1 liter)',
       'Loaf of Fresh White Bread (500g)', 'Eggs (regular) (12)',
       'Local Cheese (1kg)', 'Water (1.5 liter bottle)',
       'Bottle of Wine (Mid-Range)', 'Domestic Beer (0.5 liter bottle)',
       'Imported Beer (0.33 liter bottle)', 'Cigarettes 20 Pack (Marlboro)',
       'Chicken Breasts (Boneless, Skinless), (1kg)', 'Apples (1kg)', 'Oranges (1kg)',
       'Potato (1kg)', 'Lettuce (1 head)', 'Cappuccino (regular)',
       'Rice (white), (1kg)', 'Tomato (1kg)', 'Banana (1kg)', 'Onion (1kg)',
       'Beef Round (1kg) (or Equivalent Back Leg Red Meat)',]].mean()).mean()))

def travel(df):
    return int(round((df[['One-way Ticket (Local Transport)',
                            'Monthly Pass (Regular Price)', 'Gasoline (1 liter)',
                           'Taxi Start (Normal Tariff)', 'Taxi 1km (Normal Tariff)',
                           'Taxi 1hour Waiting (Normal Tariff)',]].mean()).mean()))

def living(df):
    return int(round((df[[ 'Volkswagen Golf',
       'Apartment (1 bedroom) in City Centre',
       'Apartment (1 bedroom) Outside of Centre',
       'Apartment (3 bedrooms) in City Centre',
       'Apartment (3 bedrooms) Outside of Centre',
       'Basic (Electricity, Heating, Cooling, Water, Garbage) for 85m2 Apartment',
         'Price per Square Meter to Buy Apartment in City Centre',
       'Price per Square Meter to Buy Apartment Outside of Centre',
        'Toyota Corolla 1.6l 97kW Comfort (Or Equivalent New Car)',]].mean()).mean()))

def lifestyle(df):
    return int(round((df[['1 min. of Prepaid Mobile Tariff Local (No Discounts or Plans)',
       'Internet (60 Mbps or More, Unlimited Data, Cable/ADSL)',
       'Fitness Club, Monthly Fee for 1 Adult',
       'Tennis Court Rent (1 Hour on Weekend)',
       'Cinema, International Release, 1 Seat',
       '1 Pair of Jeans (Levis 501 Or Similar)',
       '1 Summer Dress in a Chain Store (Zara, H&M, ...)',
       '1 Pair of Nike Running Shoes (Mid-Range)',
       '1 Pair of Men Leather Business Shoes', 
       'Meal for 2 People, Mid-range Restaurant, Three-course',
       'McMeal at McDonalds (or Equivalent Combo Meal)',]].mean()).mean()))

def education(df):
    return int(round((df[['Preschool (or Kindergarten), Full Day, Private, Monthly for 1 Child',
       'International Primary School, Yearly for 1 Child',]].mean()).mean()))

def income(df):
    return int(round((df[['Average Monthly Net Salary (After Tax)',
       'Mortgage Interest Rate in Percentages (%), Yearly, for 20 Years Fixed-Rate',]].mean()).mean()))


# Colunas com as médias de gastos (segmentado)
df['Food'] = df.apply(food, axis = 1)
df['Travel'] = df.apply(travel, axis = 1)
df['Living'] = df.apply(living, axis = 1)
df['Lifestyle'] = df.apply(lifestyle, axis = 1)
df['Education'] = df.apply(education, axis = 1)
df['Income'] = df.apply(income, axis = 1)


# Criando coluna para países
df['Country'] = df['Location'].str.split(', ')
df['Country'] = df['Country'].apply(lambda x: x[-1])


# Cidades e as médias de custos por classe
from ipywidgets import interact
@interact
def consulta(column = ['Food','Travel','Living','Lifestyle','Education','Income']):
    return df[['Country','Food','Travel','Living','Lifestyle','Education','Income']]\
        .groupby('Country').agg('mean')\
        .sort_values(by=[column], ascending=False)\
        .style.background_gradient(cmap='autumn_r')

interactive(children=(Dropdown(description='column', options=('Food', 'Travel', 'Living', 'Lifestyle', 'Educat…


# Gráficos - Top 5 países com maiores custos médios por categoria

from plotly.subplots import make_subplots
fig = make_subplots(rows=3, cols=2, subplot_titles=['Food','Travel','Living','Lifestyle','income','Education'])
fig.update_layout(height=1000, showlegend=False, title='Top 5 paises com custos mais altos')

df1 = df[['Country','Food']].groupby('Country').agg('mean').sort_values(by='Food', ascending=False).reset_index().head(5)
fig.add_bar(x=df1['Country'].head(5), y=df1['Food'], row=1, col=1)
df3 = df[['Country','Travel']].groupby('Country').agg('mean').sort_values(by='Travel', ascending=False).reset_index().head(5)
fig.add_bar(x=df3['Country'].head(5), y=df3['Travel'],row=1, col=2)
df4 = df[['Country','Living']].groupby('Country').agg('mean').sort_values(by='Living', ascending=False).reset_index().head(5)
fig.add_bar(x=df4['Country'].head(5), y=df4['Living'],row=2, col=1)
df5 = df[['Country','Lifestyle']].groupby('Country').agg('mean').sort_values(by='Lifestyle', ascending=False).reset_index().head(5)
fig.add_bar(x=df5['Country'].head(5), y=df5['Lifestyle'],row=2, col=2)
df6 = df[['Country','Income']].groupby('Country').agg('mean').sort_values(by='Income', ascending=False).reset_index().head(5)
fig.add_bar(x=df6['Country'].head(5), y=df6['Income'],row=3, col=1)
df7 = df[['Country','Education']].groupby('Country').agg('mean').sort_values(by='Education', ascending=False).reset_index().head(5)
fig.add_bar(x=df7['Country'].head(5), y=df7['Education'],row=3, col=2)


# Gráficos - Top 5 países com menores custos médios por categoria

from plotly.subplots import make_subplots
fig = make_subplots(rows=3, cols=2, subplot_titles=['Food','Travel','Living','Lifestyle','income','Education'])
fig.update_layout(height=1000, showlegend=False, title='Top 5 paises com custos mais baixos')

df1 = df[['Country','Food']].groupby('Country').agg('mean').sort_values(by='Food').reset_index().head(5)
fig.add_bar(x=df1['Country'].head(5), y=df1['Food'], row=1, col=1)
df3 = df[['Country','Travel']].groupby('Country').agg('mean').sort_values(by='Travel').reset_index().head(5)
fig.add_bar(x=df3['Country'].head(5), y=df3['Travel'],row=1, col=2)
df4 = df[['Country','Living']].groupby('Country').agg('mean').sort_values(by='Living').reset_index().head(5)
fig.add_bar(x=df4['Country'].head(5), y=df4['Living'],row=2, col=1)
df5 = df[['Country','Lifestyle']].groupby('Country').agg('mean').sort_values(by='Lifestyle').reset_index().head(5)
fig.add_bar(x=df5['Country'].head(5), y=df5['Lifestyle'],row=2, col=2)
df6 = df[['Country','Income']].groupby('Country').agg('mean').sort_values(by='Income').reset_index().head(5)
fig.add_bar(x=df6['Country'].head(5), y=df6['Income'],row=3, col=1)
df7 = df[['Country','Education']].groupby('Country').agg('mean').sort_values(by='Education').reset_index().head(5)
fig.add_bar(x=df7['Country'].head(5), y=df7['Education'],row=3, col=2)


# Classificando range dos valores por classes; maior -> menor
top_range = (df.describe().loc['min',:] / df.describe().loc['max',:]).iloc[-6:].sort_values().to_frame().reset_index()
top_range = top_range.rename(columns={'index':'Class', 0:'min / max'})
top_range['min / max'] = top_range['min / max'].apply(lambda x: round(1/x, 2))
top_range


# Função para add cores
def color_circles(val):
    if val <= df[item].quantile(.25):
        return 'forestgreen'
    elif val <= df[item].quantile(.50):
        return 'goldenrod'
    elif val <= df[item].quantile(.75):
        return 'Tomato'
    else:
        return 'darkred'


# plot Análise geoespacial 1
map = folium.Map(location=[df['latitude'].mean(),
                           df['longitude'].mean()],
                 tiles="Stamen Terrain",
                 zoom_start=2)

item = top_range.iloc[0][0]

for i in range(0, len(df)):
    Circle(location = [df.iloc[i]['latitude'], df.iloc[i]['longitude']],
           radius=120000,
           color=color_circles(df.iloc[i][item])).add_to(map)

print('Preço médio da ',item)
map

Preço médio da  Education


# plot Análise geoespacial 2
map = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()],
                 zoom_start=2)

item = top_range.iloc[1][0]


for i in range(0,len(df)):
    Circle(location=[df.iloc[i]['latitude'], df.iloc[i]['longitude']],
           radius=90000,
           color=color_circles(df.iloc[i][item])).add_to(map)

print('Preço médio da ',item)

map

Preço médio da  Travel


# plot Análise geoespacial 3
map = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()],
                 tiles='Stamen Watercolor',
                 zoom_start=2)

item = top_range.iloc[2][0]

for i in range(0,len(df)):
    Circle(location=[df.iloc[i]['latitude'], df.iloc[i]['longitude']],
           radius=100000,
           color= color_circles(df.iloc[i][item])).add_to(map)

print('Valor médio do ',item)
map

Valor médio do  Income


# plot Análise geoespacial 4
map = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()],
                 tiles='CartoDB positron',
                 zoom_start=2)

item = top_range.iloc[3][0]

for i in range(0,len(df)):
    Circle(location=[df.iloc[i]['latitude'], df.iloc[i]['longitude']],
           radius=100000,
           color= color_circles(df.iloc[i][item])).add_to(map)

print('Valor médio do ',item)
map

Valor médio do  Food


# plot Análise geoespacial 5
map = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()],
                 tiles='CartoDB dark_matter',
                 zoom_start=2)

item = top_range.iloc[4][0]

for i in range(0,len(df)):
    Circle(location=[df.iloc[i]['latitude'], df.iloc[i]['longitude']],
           radius=100000,
           tooltip=df.iloc[i]['Location'],
           color= color_circles(df.iloc[i][item])).add_to(map)

print('Valor médio do ',item)
map

Valor médio do  Living


from folium import plugins # adicionando um minimapa

# plot Análise geoespacial 6
map = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()],
                 tiles='Stamen Terrain',
                 zoom_start=3.5)

item = top_range.iloc[5][0]

for i in range(0,len(df)):
    Circle(location=[df.iloc[i]['latitude'], df.iloc[i]['longitude']],
           radius=100000,
           tooltip=df.iloc[i]['Location'],
           color= color_circles(df.iloc[i][item])).add_to(map)
    
minimap = plugins.MiniMap(tile_layer='Stamen Watercolor')    
# map.add_Legend(title='Legenda', labels=['<= 25%', '<= 50%', '<=75%', '> 75%'], colors=['forestgreen','goldenrod','Tomato','Darkred'])
map.add_child(minimap)
print('Valor médio do ',item)
map

Valor médio do  Lifestyle


# Verificando a relação entre as categorias criadas
df_class = df[['Country','Food','Living','Education','Income','Travel','Lifestyle','latitude','longitude']].groupby('Country').agg('mean').reset_index()
df_class.head(3)


correlation = df_class.iloc[:,1:7].corr()
plt.figure(figsize=(15,6))
sns.heatmap(correlation, cmap='mako_r', annot=True, linewidths=5, linecolor='DarkSlateGray')
plt.title('Correlação entre as classes de custo', size=18)
plt.yticks(rotation='horizontal')
plt.show()


# Comparação das classes entre as cidades de determinado país
df['City'] = df['Location'].str.split(', ')
df['City'] = df['City'].apply(lambda x: x[0])


# Analisando os Estados Unidos
df_US = df[df['Country']=='United States']

colunas = ['Food', 'Travel', 'Living', 'Lifestyle', 'Education','Income']
# plot
plt.figure(figsize=(16,20))
plt.suptitle('Estados Unidos', size=25)
for n, col in enumerate(colunas):
    plt.subplot(3,2,n+1)
    sns.barplot(y = df_US['City'], x= df_US[col], palette='mako')
    plt.title(f'Classe analisada : {col}', size=16)
    plt.ylabel(None)
plt.show()


# Analisando os Canada
df_CA = df[df['Country']=='Canada']

colunas = ['Food', 'Travel', 'Living', 'Lifestyle', 'Education','Income']
# plot
plt.figure(figsize=(16,20))
plt.suptitle('Canadá', size=25)
for n, col in enumerate(colunas):
    plt.subplot(3,2,n+1)
    sns.barplot(y = df_CA['City'], x= df_CA[col], palette='inferno_r')
    plt.title(f'Classe analisada : {col}', size=16)
    plt.ylabel(None)
plt.show()


# Analisando os India
df_IN = df[df['Country']=='India']

colunas = ['Food', 'Travel', 'Living', 'Lifestyle', 'Education','Income']
# plot
plt.figure(figsize=(16,20))
plt.suptitle('India', size=25)
for n, col in enumerate(colunas):
    plt.subplot(3,2,n+1)
    sns.barplot(y = df_IN['City'], x= df_IN[col], palette='Oranges_r')
    plt.title(f'Classe analisada : {col}', size=16)
    plt.ylabel(None)
plt.show()


# Qual a localização que possui a melhor pontuação geral para custo de vida:

df_best_city = df[['Location','Food', 'Travel', 'Living', 'Lifestyle', 'Education','Income','latitude','longitude']].copy()


from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0,1))
features_scaled = scaler.fit_transform(df_best_city[['Food', 'Travel', 'Living', 'Lifestyle', 'Education','Income']])
lista=[]
for i in features_scaled:
    numero = round(abs(i).mean(),3)
    lista.append(numero)
df_best_city['Score'] = lista


# Top 10 cidades com maior custo de vida:
df_best_city[['Location','Score']].sort_values(by='Score', ascending=False).head(10)


def color_Score(data, val):
    if val <= data['Score'].quantile(.25):
               return 'forestgreen'
    elif val <= data['Score'].quantile(.50):
                 return 'goldenrod'
    elif val <= data['Score'].quantile(.75):
                 return 'Tomato'
    else:
                 return 'darkred'


top_10_mais = df_best_city[['Location','Score','latitude','longitude']].sort_values(by='Score', ascending=False).head(10)

map = folium.Map(location=[top_10_mais['latitude'].mean(), top_10_mais['longitude'].mean()],
                 tiles='CartoDB positron',
                 zoom_start=2)
for i in range(0,len(top_10_mais)):
    Circle(location=[top_10_mais.iloc[i]['latitude'], top_10_mais.iloc[i]['longitude']],
           radius=150000,
           tooltip=[top_10_mais.iloc[i]['Location'],top_10_mais.iloc[i]['Score']],
           color=color_Score(top_10_mais, top_10_mais.iloc[i]['Score'])).add_to(map)

map


# Top 10 cidades com menor custo de vida:
df_best_city[['Location','Score']].sort_values(by='Score', ascending=True).head(10)


top_10_menos = df_best_city[['Location','Score','latitude','longitude']].sort_values(by='Score', ascending=True).head(10)

map = folium.Map(location=[top_10_menos['latitude'].mean(), top_10_menos['longitude'].mean()],
                 tiles='CartoDB positron',
                 zoom_start=3)
for i in range(0,len(top_10_menos)):
    Circle(location=[top_10_menos.iloc[i]['latitude'], top_10_menos.iloc[i]['longitude']],
           radius=150000,
           tooltip=[top_10_menos.iloc[i]['Location'],top_10_menos.iloc[i]['Score']],
           color=color_Score(top_10_menos, top_10_menos.iloc[i]['Score'])).add_to(map)

map

	Unnamed: 0	Saint Petersburg, Russia	Istanbul, Turkey	Izmir, Turkey	Helsinki, Finland	Chisinau, Moldova	Milan, Italy	Cairo, Egypt	Banja Luka, Bosnia And Herzegovina	Baku, Azerbaijan	Guadalajara, Mexico	Kathmandu, Nepal	Hanoi, Vietnam	Ho Chi Minh City, Vietnam	Mexico City, Mexico	Rome, Italy	Monterrey, Mexico	Yekaterinburg, Russia	Sarajevo, Bosnia And Herzegovina	Kharkiv, Ukraine	Kiev, Ukraine	Calgary, Canada	Tunis, Tunisia	Edmonton, Canada	Amsterdam, Netherlands	Belgrade, Serbia	Odessa, Ukraine	Paris, France	Eindhoven, Netherlands	Plovdiv, Bulgaria	Thessaloniki, Greece	Ottawa, Canada	Sofia, Bulgaria	Rotterdam, Netherlands	Varna, Bulgaria	Novi Sad, Serbia	Utrecht, Netherlands	Berlin, Germany	Beirut, Lebanon	Austin, TX, United States	Singapore, Singapore	Toronto, Canada	Auckland, New Zealand	Podgorica, Montenegro	Vancouver, Canada	Tokyo, Japan	Victoria, Canada	Winnipeg, Canada	Boston, MA, United States	Chicago, IL, United States	Almaty, Kazakhstan	Oslo, Norway	Frankfurt, Germany	Bratislava, Slovakia	Dallas, TX, United States	Zagreb, Croatia	Hamburg, Germany	Krakow (Cracow), Poland	Riga, Latvia	Gdansk, Poland	Santiago, Chile	Nairobi, Kenya	Abu Dhabi, United Arab Emirates	Houston, TX, United States	Tbilisi, Georgia	Dubai, United Arab Emirates	Bogota, Colombia	Brno, Czech Republic	Munich, Germany	Poznan, Poland	Las Vegas, NV, United States	London, United Kingdom	Los Angeles, CA, United States	Panama City, Panama	Seoul, South Korea	Warsaw, Poland	Prague, Czech Republic	Wroclaw, Poland	Kuala Lumpur, Malaysia	New York, NY, United States	Copenhagen, Denmark	Ljubljana, Slovenia	Chandigarh, India	Colombo, Sri Lanka	Noida, India	Kaunas, Lithuania	Athens, Greece	Phoenix, AZ, United States	Hong Kong, Hong Kong	Portland, OR, United States	Lisbon, Portugal	Beijing, China	Cape Town, South Africa	Tirana, Albania	Porto, Portugal	Durban, South Africa	Budapest, Hungary	Vilnius, Lithuania	Johannesburg, South Africa	Barcelona, Spain	San Diego, CA, United States	San Francisco, CA, United States	Lima, Peru	Seattle, WA, United States	Brasov, Romania	Bucharest, Romania	Tashkent, Uzbekistan	Ahmedabad, India	Cluj-Napoca, Romania	Madrid, Spain	Tallinn, Estonia	Bangalore, India	Iasi, Romania	Chennai, India	Doha, Qatar	Delhi, India	Gurgaon, India	Valencia, Spain	Vienna, Austria	Hyderabad, India	Montevideo, Uruguay	Tel Aviv-Yafo, Israel	Timisoara, Romania	Taipei, Taiwan	Kolkata, India	Skopje, Macedonia	Shanghai, China	Bangkok, Thailand	Mumbai, India	Reykjavik, Iceland	Amman, Jordan	Pune, India	Stockholm, Sweden	Buenos Aires, Argentina	Minsk, Belarus	San Jose, Costa Rica	Casablanca, Morocco	Lodz, Poland	Montreal, Canada	Sao Paulo, Brazil	Gothenburg, Sweden	Dublin, Ireland	Moscow, Russia	Santo Domingo, Dominican Republic	Adelaide, Australia	Zurich, Switzerland	Yerevan, Armenia	Manila, Philippines	Brisbane, Australia	Jakarta, Indonesia	Ankara, Turkey	Lviv, Ukraine	Novosibirsk, Russia	Bursa, Turkey	Brussels, Belgium	Jerusalem, Israel	Melbourne, Australia	Perth, Australia	Sydney, Australia	Alexandria, Egypt	Quito, Ecuador
0	Meal, Inexpensive Restaurant	7.34	4.58	3.06	12.0	4.67	15.0	3.38	3.58	5.27	5.25	1.99	1.94	1.94	4.77	15.0	5.75	5.88	3.57	4.50	5.63	13.75	1.92	13.75	15.0	5.39	5.65	15.0	15.0	5.10	10.0	11.00	6.14	13.0	6.12	4.84	15.0	8.5	8.98	13.48	8.33	13.75	11.95	5.0	11.69	8.19	12.05	10.31	13.47	13.47	4.77	18.70	10.0	6.0	13.48	6.72	11.0	5.91	7.0	5.91	6.94	4.43	6.12	13.48	4.67	7.33	3.30	5.95	12.0	5.90	13.47	17.49	13.47	8.98	6.42	5.91	5.95	5.91	2.87	17.97	17.40	8.0	2.54	1.49	3.80	6.0	10.0	10.77	5.78	12.58	8.5	3.91	8.91	4.10	7.0	7.47	5.99	7.0	8.47	11.0	13.48	15.95	3.26	13.48	6.27	6.28	3.59	1.90	5.23	12.0	8.0	2.53	5.23	1.52	6.18	3.80	3.80	8.75	10.00	1.90	8.41	15.54	5.23	3.61	2.53	4.87	4.56	1.78	3.80	17.96	6.34	3.16	11.40	5.99	8.93	6.29	4.45	4.72	10.31	6.58	10.45	15.0	8.80	5.10	11.19	23.12	4.70	3.56	12.38	2.62	3.82	3.75	5.72	3.82	15.0	15.56	10.22	12.43	11.81	2.81	3.59
1	Meal for 2 People, Mid-range Restaurant, Three...	29.35	15.28	12.22	65.0	20.74	60.0	17.48	22.99	23.73	23.86	11.92	15.52	17.50	23.86	55.0	23.94	29.42	20.42	18.76	22.51	42.97	12.78	48.12	65.0	24.62	20.67	50.0	56.5	20.42	35.0	55.00	25.57	50.0	20.42	17.85	65.0	40.0	53.91	44.92	39.98	55.00	50.78	25.0	55.00	40.97	49.93	41.25	62.85	53.87	23.84	80.88	50.0	30.0	44.92	33.58	50.0	23.62	40.0	23.62	34.68	26.58	48.92	53.91	18.69	48.89	19.24	25.19	60.0	23.61	53.87	64.15	58.36	35.94	31.11	28.35	29.75	25.99	16.60	76.37	80.29	35.0	10.16	14.90	12.66	30.0	40.0	53.87	46.26	44.92	35.0	26.04	35.81	24.62	30.0	25.53	29.96	35.0	31.37	40.0	53.91	71.88	21.71	62.89	20.91	27.21	15.72	8.86	23.00	40.0	45.0	12.66	20.93	7.59	49.47	15.19	12.66	35.00	45.00	8.86	31.23	64.75	20.91	24.04	10.76	16.25	26.04	23.74	15.19	109.16	38.02	12.66	66.47	20.97	27.95	42.47	20.63	23.61	44.69	28.51	75.97	60.0	36.69	34.01	40.39	92.46	22.54	17.82	49.54	16.40	15.28	18.76	22.01	11.47	60.0	62.24	49.54	56.55	54.37	14.06	31.45
2	McMeal at McDonalds (or Equivalent Combo Meal)	4.40	3.82	3.06	8.0	4.15	8.0	4.51	3.58	4.22	4.25	5.56	3.88	3.87	4.77	8.0	4.31	4.12	4.08	3.75	3.75	6.87	3.19	6.53	8.0	5.09	3.67	8.7	8.0	4.59	6.5	6.87	4.60	8.0	4.08	4.42	8.0	7.0	7.19	7.19	5.33	7.56	6.57	3.0	6.87	5.70	6.89	6.80	7.18	7.18	3.81	11.12	8.0	6.0	6.51	5.37	8.0	4.72	5.0	4.25	5.20	6.20	6.24	6.74	4.05	6.11	4.40	5.16	8.0	4.72	7.18	7.00	7.18	6.06	5.06	4.72	5.55	4.72	3.32	8.09	10.04	5.0	3.30	4.22	3.16	5.0	6.0	6.73	4.05	6.74	6.0	4.56	3.74	4.76	6.0	3.74	4.79	5.0	3.45	7.0	6.74	8.98	4.21	7.19	4.18	4.19	2.70	3.16	4.18	8.0	6.0	3.16	4.19	3.35	6.18	3.16	3.16	7.00	7.25	3.80	6.96	12.95	4.18	3.91	3.79	3.25	4.56	5.04	3.80	11.64	6.34	3.16	7.60	4.94	4.49	6.29	5.16	4.25	6.87	5.92	7.60	8.0	4.40	5.10	6.84	12.94	4.60	2.67	7.43	3.28	3.51	3.56	3.67	3.06	8.2	12.97	7.12	7.32	7.15	3.38	5.39

	Location	Meal, Inexpensive Restaurant	Meal for 2 People, Mid-range Restaurant, Three-course	McMeal at McDonalds (or Equivalent Combo Meal)	Domestic Beer (0.5 liter draught)	Imported Beer (0.33 liter bottle)	Coke/Pepsi (0.33 liter bottle)	Water (0.33 liter bottle)	Milk (regular), (1 liter)	Loaf of Fresh White Bread (500g)	Eggs (regular) (12)	Local Cheese (1kg)	Water (1.5 liter bottle)	Bottle of Wine (Mid-Range)	Domestic Beer (0.5 liter bottle)	Imported Beer (0.33 liter bottle)	Cigarettes 20 Pack (Marlboro)	One-way Ticket (Local Transport)	Chicken Breasts (Boneless, Skinless), (1kg)	Monthly Pass (Regular Price)	Gasoline (1 liter)	Volkswagen Golf	Apartment (1 bedroom) in City Centre	Apartment (1 bedroom) Outside of Centre	Apartment (3 bedrooms) in City Centre	Apartment (3 bedrooms) Outside of Centre	Basic (Electricity, Heating, Cooling, Water, Garbage) for 85m2 Apartment	1 min. of Prepaid Mobile Tariff Local (No Discounts or Plans)	Internet (60 Mbps or More, Unlimited Data, Cable/ADSL)	Fitness Club, Monthly Fee for 1 Adult	Tennis Court Rent (1 Hour on Weekend)	Cinema, International Release, 1 Seat	1 Pair of Jeans (Levis 501 Or Similar)	1 Summer Dress in a Chain Store (Zara, H&M, ...)	1 Pair of Nike Running Shoes (Mid-Range)	1 Pair of Men Leather Business Shoes	Price per Square Meter to Buy Apartment in City Centre	Price per Square Meter to Buy Apartment Outside of Centre	Average Monthly Net Salary (After Tax)	Mortgage Interest Rate in Percentages (%), Yearly, for 20 Years Fixed-Rate	Taxi Start (Normal Tariff)	Taxi 1km (Normal Tariff)	Taxi 1hour Waiting (Normal Tariff)	Apples (1kg)	Oranges (1kg)	Potato (1kg)	Lettuce (1 head)	Cappuccino (regular)	Rice (white), (1kg)	Tomato (1kg)	Banana (1kg)	Onion (1kg)	Beef Round (1kg) (or Equivalent Back Leg Red Meat)	Toyota Corolla 1.6l 97kW Comfort (Or Equivalent New Car)	Preschool (or Kindergarten), Full Day, Private, Monthly for 1 Child	International Primary School, Yearly for 1 Child
0	Saint Petersburg, Russia	7.34	29.35	4.4	2.2	2.2	0.76	0.53	0.98	0.71	1.18	7.6	0.63	5.87	0.88	1.89	2.2	0.59	3.96	42.56	0.67	19289.39	524.45	344.27	1012.53	615.19	102.17	0.03	6.96	26.27	18.33	5.14	71.86	38.25	74.88	100.72	2476.05	1507.7	645.94	10.29	1.47	0.26	4.4	1.29	1.25	0.56	0.86	1.96	0.92	1.91	0.89	0.48	7.18	19305.29	411.83	5388.86
1	Istanbul, Turkey	4.58	15.28	3.82	3.06	3.06	0.64	0.24	0.71	0.36	1.62	5.32	0.33	7.64	1.79	2.48	2.75	0.41	3.5	31.32	1.05	24443.15	291.55	177.72	545.5	314.98	59.33	0.11	14.2	25.1	15.66	3.82	36.15	25.91	61.31	50.58	1315.18	657.05	436.0	19.08	0.76	0.47	3.59	0.85	0.86	0.59	0.61	1.84	1.3	0.8	1.91	0.62	9.73	20874.72	282.94	6905.43
2	Izmir, Turkey	3.06	12.22	3.06	2.29	2.75	0.61	0.22	0.65	0.38	1.51	4.97	0.29	6.11	1.63	2.09	2.29	0.46	3.05	22.92	1.04	24443.15	212.22	128.36	353.76	225.34	51.07	0.09	12.89	20.64	11.67	3.06	33.2	22.48	52.74	42.2	856.54	508.14	392.07	22.23	0.61	0.57	3.51	0.77	0.73	0.52	0.57	1.56	1.31	0.7	1.78	0.58	8.61	20898.83	212.18	4948.41
3	Helsinki, Finland	12.0	65.0	8.0	6.5	6.75	2.66	1.89	0.96	2.27	2.02	6.87	1.54	12.0	2.23	2.95	7.7	2.8	9.42	59.7	1.54	22000.0	986.93	758.13	1819.32	1271.16	82.66	0.07	22.31	34.54	24.72	14.0	83.41	31.41	82.49	120.97	7444.54	4099.36	2382.56	1.38	6.0	1.0	47.04	2.1	1.75	0.91	2.3	3.87	2.13	2.91	1.61	1.25	12.34	24402.77	351.6	1641.0
4	Chisinau, Moldova	4.67	20.74	4.15	1.04	1.43	0.64	0.44	0.68	0.33	1.11	5.79	0.59	3.61	0.77	1.38	1.56	0.1	3.55	9.33	0.98	16335.12	244.49	177.96	428.67	330.84	113.46	0.07	8.58	26.64	12.1	5.19	56.54	35.37	86.25	94.38	904.82	589.13	286.95	9.42	1.3	0.18	3.11	0.7	1.22	0.56	0.84	1.25	0.93	1.56	1.37	0.59	5.37	17238.13	210.52	2679.3

	International Primary School, Yearly for 1 Child	latitude	longitude
0	5388.86	59.917857	30.380619
1	6905.43	41.009198	28.966219
2	4948.41	38.422455	27.131070
3	1641.00	60.167488	24.942747
4	2679.30	47.024512	28.832292

	Country	Food	Living	Education	Income	Travel	Lifestyle	latitude	longitude
0	Albania	2.0	4157.0	1225.0	171.0	5.0	29.0	41.328148	19.818444
1	Argentina	2.0	4346.0	1638.0	266.0	3.0	28.0	-34.607568	-58.437089
2	Armenia	2.0	4390.0	1581.0	154.0	3.0	30.0	40.177625	44.512617

	Location	Score
144	Zurich, Switzerland	0.790
78	New York, NY, United States	0.670
100	San Francisco, CA, United States	0.660
39	Singapore, Singapore	0.576
70	London, United Kingdom	0.559
128	Reykjavik, Iceland	0.557
102	Seattle, WA, United States	0.540
50	Oslo, Norway	0.534
47	Boston, MA, United States	0.526
71	Los Angeles, CA, United States	0.487

Projeto de Estudo em Ciência de Dados¶

Projeto - Análise de custo de vida global¶

Dados¶

	Class	min / max
0	Education	43.82
1	Travel	37.00
2	Income	36.31
3	Food	8.00
4	Living	7.64
5	Lifestyle	4.40

	Location	Score
123	Kolkata, India	0.043
106	Ahmedabad, India	0.044
112	Chennai, India	0.056
63	Tbilisi, Georgia	0.058
118	Hyderabad, India	0.060
81	Chandigarh, India	0.064
130	Pune, India	0.069
82	Colombo, Sri Lanka	0.070
114	Delhi, India	0.071
127	Mumbai, India	0.072